The subject matter disclosed herein relates to methods and systems for predicting corrosion potential, monitoring corrosive contaminant load and optimizing corrosion maintenance.
A tremendous amount of inspection and maintenance is required to combat corrosion on aircraft (or other vehicles or assets). Currently, most asset owners perform regularly scheduled maintenance actions but, unfortunately, the schedule is rarely optimized. This lack of optimization leads to costs in terms of unnecessary asset inspection/maintenance/downtime or missing corrosion that has occurred.
Asset fleet owners currently improve their maintenance performance by altering their maintenance schedules based on establishing general environmental corrosivity zones. These corrosivity zones are defined in accordance with locations of assets and their proximity to certain environmental conditions. For example, assets located in the desert (dry, low salt) will be said to exist in a low corrosivity zone and will generally require less corrosion upkeep than assets in humid, high corrosivity zones near the ocean (such as on the Florida coast). While this method is helpful on average, it does not account for the specifics of locations (e.g. is the asset sheltered?) or operational profiles (where and how is the asset operated) of each individual asset, nor does it account for microclimates within the aircraft that could be causing more localized corrosion.
Indeed, a typical method that is in use for estimating the potential for corrosion based on the severity of the environment around an asset uses average values of environmental corrosion stressors over a period of time. Most assessments of general environmental corrosivity have the form:Corrosivity*=f(T, RH, Cl, Su),where * can also be ‘Environmental Severity’ or some ‘index’ of the environmental severity and where corrosivity is reported in the units of g/m2/yr (or similar), T=average temperature, RH=average relative humidity, Cl=average chloride deposition rate and Su=average Sulfur, SO2 or pollutant deposition rate. In many assessment methods, the T and RH components may be combined into a “time of wetness” (TOW) term, which is often defined as a percent of time that RH>80% when T>0° C.
Once a corrosivity of a location is determined, asset fleet owners can calculate a prediction for average corrosion rate (CR) of the materials (e.g., aluminum) in their assets over the period of time selected. Unfortunately, the methodology of using average values of environmental stressors over the time period leads to a much lower than desirable correlation with actual corrosion that has occurred, thus making the assessment less useful.
The reason that the correlation of the typical methods is low is that a large percent of the overall long-term corrosion is driven by short term instantaneous corrosion rates which can be very high, based on the instantaneous (not average) levels for T, RH. Also, the physical relationship of corrosion rate as a function of humidity/moisture should be more of an ‘Arrhenius’ form, rather than a single ‘wet/dry’ assessment at 80% RH. That is, a humidity value of 95% will drive corrosion much more than 83%. Likewise, in some instances, RH values below 80% will still drive corrosion when there is a substantial amount of hygroscopic contaminants on the surface (that may tend to attract or retain the moisture). Moreover, since the typical CR relationships are typically valid only for external environments, one cannot easily estimate a CR for an internal space based on those algorithms. Finally, it is noted that for typical corrosion rate estimation methods, contaminant loads are based on an average loading rate and not an actual contaminant load level (e.g., Cl buildup) on a surface. Thus, with the typical methods, the effect of surface cleanings cannot be accounted for.